Digital images typically comprise an array of picture elements or pixels. In color images, each pixel represents the color at a particular point in the image. In black and white (or grayscale) images, each pixel represents the luminance level at a particular point in the image.
There are many models for encoding the color (or the gray level) associated with particular pixels in a digital image. Typically, for color images, such models involve three color model values. For example, in the well known RGB color model, each pixel is represented by a value (R) corresponding to the level of the primary color red, a value (G) corresponding to the level of the primary color green and a value (B) corresponding to the level of the primary color blue. In another common color model, referred to as the YIQ model, each pixel is represented by a value (Y) referred to as the luminance and a pair of values (I, Q) referred to as the chrominance. The YIQ model is used in the NTSC television standard. There are other many other color models which are well known to those skilled in the art. Non-limiting examples of other color models include: CMY and CMYK (used in the printing industry), YUV (used in the PAL video standard), YCbCr (used in the JPEG and MPEG standards), HSV and HSL.
In practice, digital imaging systems encode each color model value for a given pixel using a number of binary bits. The number of bits for each color model value may be referred to as the “bit depth” of that color model value. Many prior art digital imaging systems use 8-bits (i.e. an effective range of 0 to (28−1)=255) for each color model value. For example, a prior art system using an RGB color model may use an 8-bit number for each of the R, G and B color model values. The maximum number of distinct colors that can be represented in such a system is then 28×28×28=224. These digital imaging systems may be referred to as low dynamic range (LDR) systems.
Recent developments in digital imaging systems have provided digital imaging systems with the capability to display images having more than 224 distinct colors. Such digital imaging systems may be referred to as high dynamic range (HDR) systems. Some HDR imaging systems are capable of processing and/or displaying color model values with a greater bit depth (i.e. more than 8 bits are used for each color model value).
Some color models, such as the YIQ model described above, are designed to take advantage of the perception characteristics of the human eye. It has been discovered that the human eye is more perceptive to differences in luminance (Y) than to differences in chrominance (I, Q). Accordingly, some digital imaging systems may be designed to have a higher bit depth in the color model value associated with luminance (Y) and a lower bit depth in the color model values associated with chrominance (I, Q).
There is a general desire for newer generation HDR systems to be backwards compatible. Accordingly, there is a general need to provide higher bit depth imaging systems with the ability to convert and use images captured by lower dynamic range systems or images otherwise represented with a lower bit depth.
When one or more of the color model values for a pixel in a digital image is at its maximum possible value, the color model value is said to be “saturated”. For example, in a 8-bit LDR imaging system using a YIQ color model, the luminance value (Y) is saturated when it has a value of 28−1=255. Luminance saturation can occur when capturing a digital image having a very bright spot, such as a light or the sun, for example. Those skilled in the art will appreciate that saturation of any of the color model values in a digital image may involve a loss of image information. In some applications, there is a desire to reconstruct or otherwise estimate some of the image information lost when one or more of the color model values in a digital image is saturated.